Automatic Speech segmentation is an important step for building speech recognition systems. Since the speech data is non-linear in nature, capturing and dealing with the non-linear speech data is a common and challenging problem.
In the current scenario, lots of approaches have been developed to capture the non-linear speech data and processing the same for segmentation, such as techniques of supervised learning, like support vector machines.
Existing speech segmentation methods have addressed the problem using supervised learning techniques, wherein the resultant segmentation of the speech data depends upon the classifier which has been used and the training set of the speech data. There have been several attempts made to use cross recurrence plot for speech processing or analysis, finding the coarticulated or transition boundary between vowel and consonants in the speech data particularly but the problem associated with supervised learning remain the same. Another drawback associated with the supervised learning is false alarming, which does not allow the effective processing of the non-linear speech signals.
In order to achieve an accurate detection of boundary of coarticulated units from isolated speech using recurrence plot, a statistical based method and system is required which could find the coarticulated or transition boundary between vowel and consonants using recurrence plot.
However, the existing methods and systems are not capable of providing a statistical based approach for detecting boundary of coarticulated units from isolated speech using recurrence plot. The existing methods and systems particularly are not capable of providing a statistical based determinism in non-linear systems for speech processing, particularly automatic speech segmentation for building speech recognition systems.
The existing methods and systems particularly are not capable of detecting boundary of coarticulated units from isolated speech using recurrence plot. Some of above mentioned methods known to us are as follows:
U.S. Pat. No. 6,547,746B to Marino teaches about a method and apparatus for evaluating the response of a biological or nonbiological system to an external or internal stimulus such as optical, thermal, auditory, tactile, taste, electrical, magnetic, chemical, biochemical, pharmacological, hormonal, internal cellular transformations, etc. This patent does not focus on the speech processing. The patent does not teach about detecting boundary of coarticulated units from isolated speech using recurrence plot.
JP2008116588A by Dai et sl. teaches about a one-dimensional time sequence signal analysis based on unstable chaos analysis, and from a two-dimensional image created by that, a feature is extracted by calculating an HLAC coefficient. The patent does not teach about detecting boundary of coarticulated units from isolated speech using recurrence plot.
Chandrasekaran in “A nonlinear dynamic modeling for speech recognition using recurrence plot—a dynamic bayesian approach” teaches about a novel nonlinear feature extraction technique based upon Recurrence Plot. This plot not only helps in visualizing the system dynamics but also can be quantified. Chandrasekaran teaches about the conventional use of recurrence plot, but it does not teach about detecting boundary of coarticulated units from isolated speech using recurrence plot.
Eckman et al. in “Recurrence Plots of Dynamical Systems” teaches about a new diagnostic tool which is called recurrence plot; this tool tests the above assumptions, and gives useful information also when they are not satisfied. Eckman et al. does not teach about detecting boundary of coarticulated units from isolated speech using recurrence plot.
Webber et al. in “Dynamical assessment of physiological systems and states using recurrence plot strategies” illustrates how recurrence plots can take single physiological measurements, project them into multidimensional space by embedding procedures, and identify time correlations (recurrences) that are not apparent in the one-dimensional time series. Webber et al. does not teach about detecting boundary of coarticulated units from isolated speech using recurrence plot.
Potsdam Institute for Climate Impact Research in “Recurrence plots and cross recurrence plots” teaches about a recurrence plot based methods (e.g. recurrence quantification analysis) of nonlinear data analysis. It does not teach about detecting boundary of coarticulated units from isolated speech using recurrence plot.
Dale et al. in “Unraveling the Dyad: Using Recurrence Analysis to Explore Patterns of Syntactic Coordination between Children and Caregivers in Conversation” introduces recurrence analysis as a means to investigate syntactic coordination between child and caregiver. Dale et al. does not teach about detecting boundary of coarticulated units from isolated speech using recurrence plot.
Lancia et al. in “Cross-recurrence analysis of speech signals” teaches about the mean length of the diagonal lines in a CRP which constitutes a reliable index of acoustic similarity among speech signals. Lancia et al. utilizes cross recurrence and inference is drawn from single window. Lancia et al. does not teach about detecting boundary of coarticulated units from isolated speech using recurrence plot.
The above mentioned prior arts fail to disclose an efficient method and system for detecting boundary of coarticulated units from isolated speech using recurrence plot. The prior art also fail to disclose about a method and system which for could find the coarticulated or transition boundary between vowel and consonants using recurrence plot.
Thus, in the light of the above mentioned background art, it is evident that, there is a long felt need for such a solution that can provide an effective method and system for detecting boundary of coarticulated units from isolated speech using recurrence plot. There is also a need for such a solution that enables a cost effective method and system could find the coarticulated or transition boundary between vowel and consonants using recurrence plot.